{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-14T11:17:34.838245Z",
     "start_time": "2025-01-14T11:17:33.565554Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "source": [
    "a = pd.DataFrame({'a': range(7), 'b': range(7, 0, -1),\n",
    "                  'c': ['one', 'one', 'one', 'two', 'two', 'two', 'two'],\n",
    "                  'd': list(\"hjklmno\")})\n",
    "a"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T11:17:34.850113Z",
     "start_time": "2025-01-14T11:17:34.839241Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   a  b    c  d\n",
       "0  0  7  one  h\n",
       "1  1  6  one  j\n",
       "2  2  5  one  k\n",
       "3  3  4  two  l\n",
       "4  4  3  two  m\n",
       "5  5  2  two  n\n",
       "6  6  1  two  o"
      ],
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>a</th>\n",
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       "    </tr>\n",
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       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>two</td>\n",
       "      <td>n</td>\n",
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       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
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       "      <td>o</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "source": [
    "c = a.copy()\n",
    "a.index = list('abcdefg')\n",
    "print(c)\n",
    "print('-' * 50)\n",
    "print(a)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T11:17:34.858723Z",
     "start_time": "2025-01-14T11:17:34.850628Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a  b    c  d\n",
      "0  0  7  one  h\n",
      "1  1  6  one  j\n",
      "2  2  5  one  k\n",
      "3  3  4  two  l\n",
      "4  4  3  two  m\n",
      "5  5  2  two  n\n",
      "6  6  1  two  o\n",
      "--------------------------------------------------\n",
      "   a  b    c  d\n",
      "a  0  7  one  h\n",
      "b  1  6  one  j\n",
      "c  2  5  one  k\n",
      "d  3  4  two  l\n",
      "e  4  3  two  m\n",
      "f  5  2  two  n\n",
      "g  6  1  two  o\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "source": [
    "c.values.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T11:17:34.862775Z",
     "start_time": "2025-01-14T11:17:34.859717Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 4)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "source": [
    "print(a)\n",
    "print('-' * 50)\n",
    "a.set_index(['c'], inplace=True)\n",
    "a"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T11:17:34.870684Z",
     "start_time": "2025-01-14T11:17:34.863768Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a  b    c  d\n",
      "a  0  7  one  h\n",
      "b  1  6  one  j\n",
      "c  2  5  one  k\n",
      "d  3  4  two  l\n",
      "e  4  3  two  m\n",
      "f  5  2  two  n\n",
      "g  6  1  two  o\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "     a  b  d\n",
       "c           \n",
       "one  0  7  h\n",
       "one  1  6  j\n",
       "one  2  5  k\n",
       "two  3  4  l\n",
       "two  4  3  m\n",
       "two  5  2  n\n",
       "two  6  1  o"
      ],
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>one</th>\n",
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       "      <td>h</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <th>two</th>\n",
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       "      <td>o</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-14T11:17:34.875452Z",
     "start_time": "2025-01-14T11:17:34.871682Z"
    }
   },
   "cell_type": "code",
   "source": "a.columns",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'd'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-14T11:17:34.879761Z",
     "start_time": "2025-01-14T11:17:34.876447Z"
    }
   },
   "cell_type": "code",
   "source": "a.index",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['one', 'one', 'one', 'two', 'two', 'two', 'two'], dtype='object', name='c')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "source": [
    "pd.date_range(start=\"20190101\", end=\"20190201\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T11:17:34.884757Z",
     "start_time": "2025-01-14T11:17:34.880756Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',\n",
       "               '2019-01-05', '2019-01-06', '2019-01-07', '2019-01-08',\n",
       "               '2019-01-09', '2019-01-10', '2019-01-11', '2019-01-12',\n",
       "               '2019-01-13', '2019-01-14', '2019-01-15', '2019-01-16',\n",
       "               '2019-01-17', '2019-01-18', '2019-01-19', '2019-01-20',\n",
       "               '2019-01-21', '2019-01-22', '2019-01-23', '2019-01-24',\n",
       "               '2019-01-25', '2019-01-26', '2019-01-27', '2019-01-28',\n",
       "               '2019-01-29', '2019-01-30', '2019-01-31', '2019-02-01'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "source": "pd.date_range(start=\"20250107\", periods=10, freq='B')",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T11:17:34.889601Z",
     "start_time": "2025-01-14T11:17:34.885757Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2025-01-07', '2025-01-08', '2025-01-09', '2025-01-10',\n",
       "               '2025-01-13', '2025-01-14', '2025-01-15', '2025-01-16',\n",
       "               '2025-01-17', '2025-01-20'],\n",
       "              dtype='datetime64[ns]', freq='B')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "source": "pd.date_range(start=\"20190101\", periods=10, freq='ME')",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T11:17:34.893859Z",
     "start_time": "2025-01-14T11:17:34.889601Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-01-31', '2019-02-28', '2019-03-31', '2019-04-30',\n",
       "               '2019-05-31', '2019-06-30', '2019-07-31', '2019-08-31',\n",
       "               '2019-09-30', '2019-10-31'],\n",
       "              dtype='datetime64[ns]', freq='ME')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "source": "pd.date_range(start=\"20190101\", periods=10, freq='MS')",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T11:17:34.898048Z",
     "start_time": "2025-01-14T11:17:34.894371Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-01-01', '2019-02-01', '2019-03-01', '2019-04-01',\n",
       "               '2019-05-01', '2019-06-01', '2019-07-01', '2019-08-01',\n",
       "               '2019-09-01', '2019-10-01'],\n",
       "              dtype='datetime64[ns]', freq='MS')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "source": "pd.date_range(start=\"20230710\", periods=10, freq='W')",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T11:17:34.902650Z",
     "start_time": "2025-01-14T11:17:34.898048Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2023-07-16', '2023-07-23', '2023-07-30', '2023-08-06',\n",
       "               '2023-08-13', '2023-08-20', '2023-08-27', '2023-09-03',\n",
       "               '2023-09-10', '2023-09-17'],\n",
       "              dtype='datetime64[ns]', freq='W-SUN')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "source": [
    "s = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000'] * 5)\n",
    "s"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T11:17:34.906704Z",
     "start_time": "2025-01-14T11:17:34.902650Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     3/11/2000\n",
       "1     3/12/2000\n",
       "2     3/13/2000\n",
       "3     3/11/2000\n",
       "4     3/12/2000\n",
       "5     3/13/2000\n",
       "6     3/11/2000\n",
       "7     3/12/2000\n",
       "8     3/13/2000\n",
       "9     3/11/2000\n",
       "10    3/12/2000\n",
       "11    3/13/2000\n",
       "12    3/11/2000\n",
       "13    3/12/2000\n",
       "14    3/13/2000\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "code",
   "source": "pd.to_datetime(s)",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T11:17:34.913498Z",
     "start_time": "2025-01-14T11:17:34.907699Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2000-03-11\n",
       "1    2000-03-12\n",
       "2    2000-03-13\n",
       "3    2000-03-11\n",
       "4    2000-03-12\n",
       "5    2000-03-13\n",
       "6    2000-03-11\n",
       "7    2000-03-12\n",
       "8    2000-03-13\n",
       "9    2000-03-11\n",
       "10   2000-03-12\n",
       "11   2000-03-13\n",
       "12   2000-03-11\n",
       "13   2000-03-12\n",
       "14   2000-03-13\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "source": [
    "df = pd.read_csv(\"./911.csv\")\n",
    "df[\"timeStamp\"] = pd.to_datetime(df[\"timeStamp\"])\n",
    "temp_list = df[\"title\"].str.split(\": \").tolist()\n",
    "cate_list = [i[0] for i in temp_list]\n",
    "df[\"cate\"] = pd.DataFrame(np.array(cate_list).reshape((df.shape[0], 1)))\n",
    "df.set_index(\"timeStamp\", inplace=True)\n",
    "df.head(10)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T11:17:35.987284Z",
     "start_time": "2025-01-14T11:17:34.914495Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                           lat        lng  \\\n",
       "timeStamp                                   \n",
       "2015-12-10 17:10:52  40.297876 -75.581294   \n",
       "2015-12-10 17:29:21  40.258061 -75.264680   \n",
       "2015-12-10 14:39:21  40.121182 -75.351975   \n",
       "2015-12-10 16:47:36  40.116153 -75.343513   \n",
       "2015-12-10 16:56:52  40.251492 -75.603350   \n",
       "2015-12-10 15:39:04  40.253473 -75.283245   \n",
       "2015-12-10 16:46:48  40.182111 -75.127795   \n",
       "2015-12-10 16:17:05  40.217286 -75.405182   \n",
       "2015-12-10 16:51:42  40.289027 -75.399590   \n",
       "2015-12-10 17:35:41  40.102398 -75.291458   \n",
       "\n",
       "                                                                  desc  \\\n",
       "timeStamp                                                                \n",
       "2015-12-10 17:10:52  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "2015-12-10 17:29:21  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2015-12-10 14:39:21  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "2015-12-10 16:47:36  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "2015-12-10 16:56:52  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "2015-12-10 15:39:04  CANNON AVE & W 9TH ST;  LANSDALE; Station 345;...   \n",
       "2015-12-10 16:46:48  LAUREL AVE & OAKDALE AVE;  HORSHAM; Station 35...   \n",
       "2015-12-10 16:17:05  COLLEGEVILLE RD & LYWISKI RD;  SKIPPACK; Stati...   \n",
       "2015-12-10 16:51:42  MAIN ST & OLD SUMNEYTOWN PIKE;  LOWER SALFORD;...   \n",
       "2015-12-10 17:35:41  BLUEROUTE  & RAMP I476 NB TO CHEMICAL RD; PLYM...   \n",
       "\n",
       "                         zip                        title                twp  \\\n",
       "timeStamp                                                                      \n",
       "2015-12-10 17:10:52  19525.0       EMS: BACK PAINS/INJURY        NEW HANOVER   \n",
       "2015-12-10 17:29:21  19446.0      EMS: DIABETIC EMERGENCY  HATFIELD TOWNSHIP   \n",
       "2015-12-10 14:39:21  19401.0          Fire: GAS-ODOR/LEAK         NORRISTOWN   \n",
       "2015-12-10 16:47:36  19401.0       EMS: CARDIAC EMERGENCY         NORRISTOWN   \n",
       "2015-12-10 16:56:52      NaN               EMS: DIZZINESS   LOWER POTTSGROVE   \n",
       "2015-12-10 15:39:04  19446.0             EMS: HEAD INJURY           LANSDALE   \n",
       "2015-12-10 16:46:48  19044.0         EMS: NAUSEA/VOMITING            HORSHAM   \n",
       "2015-12-10 16:17:05  19426.0   EMS: RESPIRATORY EMERGENCY           SKIPPACK   \n",
       "2015-12-10 16:51:42  19438.0        EMS: SYNCOPAL EPISODE      LOWER SALFORD   \n",
       "2015-12-10 17:35:41  19462.0  Traffic: VEHICLE ACCIDENT -           PLYMOUTH   \n",
       "\n",
       "                                                         addr  e     cate  \n",
       "timeStamp                                                                  \n",
       "2015-12-10 17:10:52                    REINDEER CT & DEAD END  1      EMS  \n",
       "2015-12-10 17:29:21                BRIAR PATH & WHITEMARSH LN  1      EMS  \n",
       "2015-12-10 14:39:21                                  HAWS AVE  1     Fire  \n",
       "2015-12-10 16:47:36                        AIRY ST & SWEDE ST  1      EMS  \n",
       "2015-12-10 16:56:52                  CHERRYWOOD CT & DEAD END  1      EMS  \n",
       "2015-12-10 15:39:04                     CANNON AVE & W 9TH ST  1      EMS  \n",
       "2015-12-10 16:46:48                  LAUREL AVE & OAKDALE AVE  1      EMS  \n",
       "2015-12-10 16:17:05              COLLEGEVILLE RD & LYWISKI RD  1      EMS  \n",
       "2015-12-10 16:51:42             MAIN ST & OLD SUMNEYTOWN PIKE  1      EMS  \n",
       "2015-12-10 17:35:41  BLUEROUTE  & RAMP I476 NB TO CHEMICAL RD  1  Traffic  "
      ],
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>cate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timeStamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-10 17:10:52</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10 17:29:21</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10 14:39:21</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10 16:47:36</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-10 16:56:52</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10 15:39:04</th>\n",
       "      <td>40.253473</td>\n",
       "      <td>-75.283245</td>\n",
       "      <td>CANNON AVE &amp; W 9TH ST;  LANSDALE; Station 345;...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: HEAD INJURY</td>\n",
       "      <td>LANSDALE</td>\n",
       "      <td>CANNON AVE &amp; W 9TH ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10 16:46:48</th>\n",
       "      <td>40.182111</td>\n",
       "      <td>-75.127795</td>\n",
       "      <td>LAUREL AVE &amp; OAKDALE AVE;  HORSHAM; Station 35...</td>\n",
       "      <td>19044.0</td>\n",
       "      <td>EMS: NAUSEA/VOMITING</td>\n",
       "      <td>HORSHAM</td>\n",
       "      <td>LAUREL AVE &amp; OAKDALE AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10 16:17:05</th>\n",
       "      <td>40.217286</td>\n",
       "      <td>-75.405182</td>\n",
       "      <td>COLLEGEVILLE RD &amp; LYWISKI RD;  SKIPPACK; Stati...</td>\n",
       "      <td>19426.0</td>\n",
       "      <td>EMS: RESPIRATORY EMERGENCY</td>\n",
       "      <td>SKIPPACK</td>\n",
       "      <td>COLLEGEVILLE RD &amp; LYWISKI RD</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10 16:51:42</th>\n",
       "      <td>40.289027</td>\n",
       "      <td>-75.399590</td>\n",
       "      <td>MAIN ST &amp; OLD SUMNEYTOWN PIKE;  LOWER SALFORD;...</td>\n",
       "      <td>19438.0</td>\n",
       "      <td>EMS: SYNCOPAL EPISODE</td>\n",
       "      <td>LOWER SALFORD</td>\n",
       "      <td>MAIN ST &amp; OLD SUMNEYTOWN PIKE</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-10 17:35:41</th>\n",
       "      <td>40.102398</td>\n",
       "      <td>-75.291458</td>\n",
       "      <td>BLUEROUTE  &amp; RAMP I476 NB TO CHEMICAL RD; PLYM...</td>\n",
       "      <td>19462.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>PLYMOUTH</td>\n",
       "      <td>BLUEROUTE  &amp; RAMP I476 NB TO CHEMICAL RD</td>\n",
       "      <td>1</td>\n",
       "      <td>Traffic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(20, 8), dpi=80)\n",
    "for group_name, group_data in df.groupby(by=\"cate\"):\n",
    "    print(group_name)\n",
    "    print(group_data.shape)\n",
    "    count_by_month = group_data.resample(\"ME\").count()[\"title\"]\n",
    "    print(count_by_month)\n",
    "    print('-' * 50)\n",
    "    _x = count_by_month.index\n",
    "    print(_x)\n",
    "    print('-' * 50)\n",
    "    _y = count_by_month.values\n",
    "    _x = [i.strftime(\"%Y-%m-%d\") for i in _x]\n",
    "    print(_x)\n",
    "    print('-' * 50)\n",
    "    plt.plot(_x, _y, label=group_name)\n",
    "print('-' * 50)\n",
    "plt.xticks(range(len(_x)), _x, rotation=45)\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T13:28:35.817637Z",
     "start_time": "2025-01-14T13:28:35.449450Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EMS\n",
      "(124840, 9)\n",
      "timeStamp\n",
      "2015-12-31    3898\n",
      "2016-01-31    6063\n",
      "2016-02-29    5413\n",
      "2016-03-31    5832\n",
      "2016-04-30    5684\n",
      "2016-05-31    5730\n",
      "2016-06-30    5720\n",
      "2016-07-31    6029\n",
      "2016-08-31    6005\n",
      "2016-09-30    5750\n",
      "2016-10-31    6039\n",
      "2016-11-30    5838\n",
      "2016-12-31    6024\n",
      "2017-01-31    6082\n",
      "2017-02-28    5417\n",
      "2017-03-31    5913\n",
      "2017-04-30    5780\n",
      "2017-05-31    5948\n",
      "2017-06-30    6030\n",
      "2017-07-31    5974\n",
      "2017-08-31    5882\n",
      "2017-09-30    3789\n",
      "Freq: ME, Name: title, dtype: int64\n",
      "--------------------------------------------------\n",
      "DatetimeIndex(['2015-12-31', '2016-01-31', '2016-02-29', '2016-03-31',\n",
      "               '2016-04-30', '2016-05-31', '2016-06-30', '2016-07-31',\n",
      "               '2016-08-31', '2016-09-30', '2016-10-31', '2016-11-30',\n",
      "               '2016-12-31', '2017-01-31', '2017-02-28', '2017-03-31',\n",
      "               '2017-04-30', '2017-05-31', '2017-06-30', '2017-07-31',\n",
      "               '2017-08-31', '2017-09-30'],\n",
      "              dtype='datetime64[ns]', name='timeStamp', freq='ME')\n",
      "--------------------------------------------------\n",
      "['2015-12-31', '2016-01-31', '2016-02-29', '2016-03-31', '2016-04-30', '2016-05-31', '2016-06-30', '2016-07-31', '2016-08-31', '2016-09-30', '2016-10-31', '2016-11-30', '2016-12-31', '2017-01-31', '2017-02-28', '2017-03-31', '2017-04-30', '2017-05-31', '2017-06-30', '2017-07-31', '2017-08-31', '2017-09-30']\n",
      "--------------------------------------------------\n",
      "Fire\n",
      "(37432, 9)\n",
      "timeStamp\n",
      "2015-12-31    1095\n",
      "2016-01-31    1904\n",
      "2016-02-29    1868\n",
      "2016-03-31    1589\n",
      "2016-04-30    1717\n",
      "2016-05-31    1573\n",
      "2016-06-30    1787\n",
      "2016-07-31    1898\n",
      "2016-08-31    1907\n",
      "2016-09-30    1793\n",
      "2016-10-31    1930\n",
      "2016-11-30    1765\n",
      "2016-12-31    1846\n",
      "2017-01-31    1658\n",
      "2017-02-28    1462\n",
      "2017-03-31    1634\n",
      "2017-04-30    1614\n",
      "2017-05-31    1670\n",
      "2017-06-30    1986\n",
      "2017-07-31    1754\n",
      "2017-08-31    1862\n",
      "2017-09-30    1120\n",
      "Freq: ME, Name: title, dtype: int64\n",
      "--------------------------------------------------\n",
      "DatetimeIndex(['2015-12-31', '2016-01-31', '2016-02-29', '2016-03-31',\n",
      "               '2016-04-30', '2016-05-31', '2016-06-30', '2016-07-31',\n",
      "               '2016-08-31', '2016-09-30', '2016-10-31', '2016-11-30',\n",
      "               '2016-12-31', '2017-01-31', '2017-02-28', '2017-03-31',\n",
      "               '2017-04-30', '2017-05-31', '2017-06-30', '2017-07-31',\n",
      "               '2017-08-31', '2017-09-30'],\n",
      "              dtype='datetime64[ns]', name='timeStamp', freq='ME')\n",
      "--------------------------------------------------\n",
      "['2015-12-31', '2016-01-31', '2016-02-29', '2016-03-31', '2016-04-30', '2016-05-31', '2016-06-30', '2016-07-31', '2016-08-31', '2016-09-30', '2016-10-31', '2016-11-30', '2016-12-31', '2017-01-31', '2017-02-28', '2017-03-31', '2017-04-30', '2017-05-31', '2017-06-30', '2017-07-31', '2017-08-31', '2017-09-30']\n",
      "--------------------------------------------------\n",
      "Traffic\n",
      "(87465, 9)\n",
      "timeStamp\n",
      "2015-12-31    2923\n",
      "2016-01-31    5129\n",
      "2016-02-29    4115\n",
      "2016-03-31    3638\n",
      "2016-04-30    3886\n",
      "2016-05-31    4071\n",
      "2016-06-30    4225\n",
      "2016-07-31    4161\n",
      "2016-08-31    3992\n",
      "2016-09-30    4126\n",
      "2016-10-31    4533\n",
      "2016-11-30    4488\n",
      "2016-12-31    4292\n",
      "2017-01-31    3865\n",
      "2017-02-28    3388\n",
      "2017-03-31    4137\n",
      "2017-04-30    3662\n",
      "2017-05-31    4101\n",
      "2017-06-30    4317\n",
      "2017-07-31    4040\n",
      "2017-08-31    4009\n",
      "2017-09-30    2367\n",
      "Freq: ME, Name: title, dtype: int64\n",
      "--------------------------------------------------\n",
      "DatetimeIndex(['2015-12-31', '2016-01-31', '2016-02-29', '2016-03-31',\n",
      "               '2016-04-30', '2016-05-31', '2016-06-30', '2016-07-31',\n",
      "               '2016-08-31', '2016-09-30', '2016-10-31', '2016-11-30',\n",
      "               '2016-12-31', '2017-01-31', '2017-02-28', '2017-03-31',\n",
      "               '2017-04-30', '2017-05-31', '2017-06-30', '2017-07-31',\n",
      "               '2017-08-31', '2017-09-30'],\n",
      "              dtype='datetime64[ns]', name='timeStamp', freq='ME')\n",
      "--------------------------------------------------\n",
      "['2015-12-31', '2016-01-31', '2016-02-29', '2016-03-31', '2016-04-30', '2016-05-31', '2016-06-30', '2016-07-31', '2016-08-31', '2016-09-30', '2016-10-31', '2016-11-30', '2016-12-31', '2017-01-31', '2017-02-28', '2017-03-31', '2017-04-30', '2017-05-31', '2017-06-30', '2017-07-31', '2017-08-31', '2017-09-30']\n",
      "--------------------------------------------------\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 24
  },
  {
   "cell_type": "code",
   "source": [
    "dict_obj = {'key1': ['a', 'b', 'a', 'b',\n",
    "                     'a', 'b', 'a', 'a'],\n",
    "            'key2': ['one', 'one', 'two', 'three',\n",
    "                     'two', 'two', 'one', 'three'],\n",
    "            'data1': np.random.randint(1, 10, 8),\n",
    "            'data2': np.random.randint(1, 10, 8)}\n",
    "df_obj = pd.DataFrame(dict_obj)\n",
    "print(df_obj)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T13:31:32.619541Z",
     "start_time": "2025-01-14T13:31:32.614805Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  key1   key2  data1  data2\n",
      "0    a    one      3      5\n",
      "1    b    one      8      3\n",
      "2    a    two      8      5\n",
      "3    b  three      5      7\n",
      "4    a    two      6      4\n",
      "5    b    two      4      4\n",
      "6    a    one      6      1\n",
      "7    a  three      2      3\n"
     ]
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-14T13:39:10.437302Z",
     "start_time": "2025-01-14T13:39:10.432575Z"
    }
   },
   "cell_type": "code",
   "source": [
    "k1_sum = df_obj.groupby('key1').mean(numeric_only=True).add_prefix('mean_')\n",
    "print(k1_sum)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      mean_data1  mean_data2\n",
      "key1                        \n",
      "a       5.000000    3.600000\n",
      "b       5.666667    4.666667\n"
     ]
    }
   ],
   "execution_count": 28
  },
  {
   "cell_type": "code",
   "source": [
    "k1_sum_tf = df_obj.loc[:, ['key1', 'data1', 'data2']].groupby('key1').transform('mean').add_prefix('mean_')\n",
    "k1_sum_tf"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T13:42:51.453641Z",
     "start_time": "2025-01-14T13:42:51.446493Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   mean_data1  mean_data2\n",
       "0    5.000000    3.600000\n",
       "1    5.666667    4.666667\n",
       "2    5.000000    3.600000\n",
       "3    5.666667    4.666667\n",
       "4    5.000000    3.600000\n",
       "5    5.666667    4.666667\n",
       "6    5.000000    3.600000\n",
       "7    5.000000    3.600000"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_data1</th>\n",
       "      <th>mean_data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.666667</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.666667</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.666667</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 33
  },
  {
   "cell_type": "code",
   "source": [
    "del df_obj['key2']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T13:43:50.013365Z",
     "start_time": "2025-01-14T13:43:50.010359Z"
    }
   },
   "outputs": [],
   "execution_count": 34
  },
  {
   "cell_type": "code",
   "source": "df_obj.groupby('key1').transform('mean')",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T13:44:15.917060Z",
     "start_time": "2025-01-14T13:44:15.911119Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      data1     data2\n",
       "0  5.000000  3.600000\n",
       "1  5.666667  4.666667\n",
       "2  5.000000  3.600000\n",
       "3  5.666667  4.666667\n",
       "4  5.000000  3.600000\n",
       "5  5.666667  4.666667\n",
       "6  5.000000  3.600000\n",
       "7  5.000000  3.600000"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.666667</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.666667</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.666667</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 36
  },
  {
   "cell_type": "code",
   "source": [
    "def diff_mean(s):\n",
    "    return s - s.mean()\n",
    "print(df_obj.groupby('key1').transform(diff_mean))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T13:44:38.654183Z",
     "start_time": "2025-01-14T13:44:38.647484Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      data1     data2\n",
      "0 -2.000000  1.400000\n",
      "1  2.333333 -1.666667\n",
      "2  3.000000  1.400000\n",
      "3 -0.666667  2.333333\n",
      "4  1.000000  0.400000\n",
      "5 -1.666667 -0.666667\n",
      "6  1.000000 -2.600000\n",
      "7 -3.000000 -0.600000\n"
     ]
    }
   ],
   "execution_count": 37
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": ""
  }
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